CNN Remote Sensing and Satellite Image Analysis

References

[1] Achlioptas, P., Diamanti, O., Mitliagkas, I., & Guibas, L. (2017). Representation Learning and Adversarial Generation of 3D Point Clouds. 35th International Conference on Machine Learning, ICML 2018, 1, 67–85. Retrieved from https://arxiv.org/abs/1707.02392v1

[2] Ando, H., Niitsu, Y., Hirasawa, M., Teduka, H., & Yajima, M. (2016). Improvements of classification accuracy of film defects by using GPU-accelerated image processing and machine learning frameworks. In Proceedings - NICOGRAPH International 2016, NicoInt 2016 (pp. 83–87). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/NicoInt.2016.15

[3] Assendorp, J. P., & Deep. (2017). Deep learning for anomaly detection in multivariate time series data. The Ninth International Conferences on Pervasive Patterns and Applications Defect 2017, 13(1), 1–12. Retrieved from https://www.metaljournal.com.ua/assets/Journal/english-edition/MMI_2015_7/045

[4] Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184

[5] Christiansen, P., Nielsen, L. N., Steen, K. A., Jørgensen, R. N., & Karstoft, H. (2016). DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field. Sensors (Switzerland), 16(11). https://doi.org/10.3390/s16111904

[6] Ge, R., Zhu, Y., Xiao, Y., & Chen, Z. (2017). The subway pantograph detection using modified faster R-CNN. In Communications in Computer and Information Science (Vol. 685, pp. 197–204). Springer Verlag. https://doi.org/10.1007/978-981-10-4211-9_20

[7] Han, Z., Wei, B., Mercado, A., Leung, S., & Li, S. (2018). Spine-GAN: Semantic segmentation of multiple spinal structures. Medical Image Analysis, 50,23–35. https://doi.org/10.1016/j.media.2018.08.005

[8] Jabez, J., & Muthukumar, B. (2015). Detection Approach. Procedia - Procedia Computer Science, 48(Iccc), 338–346. Retrieved from http://dx.doi.org/10.1016/j.procs.2015.04.191

[9] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[10] Kaushik, P., Jain, M., & Jain, A. (2018). A pixel-based digital medical images protection using genetic algorithm. International Journal of Electronics and Communication Engineering, 31-37. http://www.irphouse.com/ijece18/ijecev11n1_05.pdf

[11] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application. https://ijsetr.com/uploads/136245IJSETR17012-283.pdf

[12] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[13] Kaushik, P., & Jain, M. (2018). Design of low power CMOS low pass filter for biomedical application. International Journal of Electrical Engineering & Technology (IJEET), 9(5).

[14] Kaushik, P., Jain, M., & Jain, A. (2018). A pixel-based digital medical images protection using genetic algorithm. International Journal of Electronics and Communication Engineering, 31-37. http://www.irphouse.com/ijece18/ijecev11n1_05.pdf

[15] Kaushik, P., Jain, M., & Shah, A. (2018). A Low Power Low Voltage CMOS Based Operational Transconductance Amplifier for Biomedical Application. https://ijsetr.com/uploads/136245IJSETR17012-283.pdf

[16] Kaushik, P., & Jain, M. A Low Power SRAM Cell for High Speed Applications Using 90nm Technology. Csjournals. Com, 10. https://www.csjournals.com/IJEE/PDF10-2/66.%20Puneet.pdf

[17] Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., … Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010

[18] Kim, J. Y., Bu, S. J., & Cho, S. B. (2017). Malware detection using deep transferred generative adversarial networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10634 LNCS, pp. 556–564). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_58

[19] Kim, J. Y., Bu, S. J., & Cho, S. B. (2018). Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Information Sciences, 460–461, 83–102. https://doi.org/10.1016/j.ins.2018.04.092

[20] Kos, J., Fischer, I., & Song, D. (2018). Adversarial examples for generative models. In Proceedings - 2018 IEEE Symposium on Security and Privacy Workshops, SPW 2018 (pp. 36–42). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SPW.2018.00014

[21] Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Autoencoding beyond pixels using a learned similarity metric. In 33rd International Conference on Machine Learning, ICML 2016 (Vol. 4, pp. 2341–2349). International Machine Learning Society (IMLS).

[22] Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2016). Vae/Gan. Icml, 4, 2341–2349.

[23] Li, W., Wu, G., & Du, Q. (2017). Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters, 14(5), 597–601. https://doi.org/10.1109/LGRS.2017.2657818

[24] Li, Y., Min, M. R., Shen, D., Carlson, D., & Carin, L. (2018). Video generation from text. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7065–7072). AAAI press. https://doi.org/10.1609/aaai.v32i1.12233

[25] Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. International Conference on Learning Representations, ICLR.

[26] Makhzani, A., & Frey, B. (2017). PixelGAN autoencoders. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 1976–1986). Neural information processing systems foundation.

[27] Nagahara, H., Umeda, K., & Yamashita, A. (2017). Thirteenth international conference on quality control by artificial vision 2017. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 10338). https://doi.org/10.1117/12.2277475

[28] Puneet Kaushik, Mohit Jain , Gayatri Patidar, Paradayil Rhea Eapen, Chandra Prabha Sharma (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www.csjournals.com/IJEE/PDF10-2/64.%20Puneet.pdf

[29] Puneet Kaushik, Mohit Jain , Gayatri Patidar, Paradayil Rhea Eapen, Chandra Prabha Sharma (2018). Smart Floor Cleaning Robot Using Android. International Journal of Electronics Engineering. https://www.csjournals.com/IJEE/PDF10-2/64.%20Puneet.pdf

[30] Sabokrou, M., Fayyaz, M., Fathy, M., & Klette, R. (2017). Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes. IEEE Transactions on Image Processing, 26(4), 1992–2004. https://doi.org/10.1109/TIP.2017.2670780

[31] Sørensen, R. A., Rasmussen, J., Nielsen, J., & Jørgensen, R. N. (2017). Thistle detection using convolutional neural networks: EFITA 2017 Presentation. EFITA WCCA Congress. 2.-6. June 2017, 16(July), 1–15. Retrieved from https://www.mdpi.com/165332

[32] Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., & Sutton, C. (2017). VEEGAN: Reducing mode collapse in GANs using implicit variational learning. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 3309–3319). Neural information processing systems foundation.

[33] Stentoumis, C., Protopapadakis, E., Doulamis, A., & Doulamis, N. (2016). A holistic approach for inspection of civil infrastructures based on computer vision techniques. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 41, pp. 131–138). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprsarchives-XLI-B5-131-2016

[34] Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., & Hospedales, T. M. (2018). Learning to Compare: Relation Network for Few-Shot Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1199–1208). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00131

[35] T., J. (2018). Comparative Study of GAN and VAE. International Journal of Computer Applications, 182(22), 1–5. https://doi.org/10.5120/ijca2018918039

[36] Tawara, N., Kobayashi, T., Fujieda, M., Katagiri, K., Yazu, T., & Ogawa, T. (2018). Adversarial autoencoder for reducing nonlinear distortion. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings (pp. 1669–1673). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.23919/APSIPA.2018.8659540

[37] V.N., E., & U.R., I. (2016). Modeling approaches for time series forecasting and anomaly detection. Animal Molecular Breeding, 8–13.

[38] Wang, H., Qin, Z., & Wan, T. (2018). Text generation based on generative adversarial nets with latent variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10938 LNAI, pp. 92–103). Springer Verlag. https://doi.org/10.1007/978-3-319-93037-4_8

[39] Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018, August 1). Convolutional neural networks: an overview and application in radiology. Insights into Imaging. Springer Verlag. https://doi.org/10.1007/s13244-018-0639-9

[40] Zhao, Y., Deng, B., Huang, J., Lu, H., & Hua, X. S. (2017). Stylized adversarial autoencoder for image generation. In MM 2017 - Proceedings of the 2017 ACM Multimedia Conference (pp. 244–251). Association for Computing Machinery, Inc. https://doi.org/10.1145/3123266.3123450

[41] Zhao, Z., Li, B., Dong, R., & Zhao, P. (2018). A surface defect detection method based on positive samples. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11013 LNAI, pp. 473–481). Springer Verlag. https://doi.org/10.1007/978-3-319-97310-4_54

[42] Zheng, Y. J., Zhou, X. H., Sheng, W. G., Xue, Y., & Chen, S. Y. (2018). Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks, 102, 78–86. https://doi.org/10.1016/j.neunet.2018.02.015

[43] Zhou, Y., & Tuzel, O. (2018). VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 4490–4499). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00472

[44] Puneet Kaushik, Mohit Jain, Study and Analysis of Image Encryption Algorithm Based on Arnold Transformation. International Journal of Computer Engineering and Technology, 9(5), 2018, pp. 59-63. http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5

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@article{1709002,
      author = {Rajat Yadav, Raghav Kumar, Aniket Tripathi},
      title = {CNN Remote Sensing and Satellite Image Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {174-185},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709002.pdf},
      abstract = {CNNs have made it possible for remote sensing to process data more accurately and without manual effort which is especially beneficial for satellite images. Rather than manually including handmade features, CNNs find patterns in the raw data and perform better and more easily on large and complicated data. It is especially helpful in spotting changes in land use, deforestation, new urban growth and harm to the environment. Many remote sensing applications, include land cover classification, detecting objects, examining plants and finding change over time, have shown that CNNs perform very well. Tasks like urban planning and precision farming depend on spatial precision which is offered by advanced CNN architectures like U-Net, ResNet and DeepLab. They can handle data gathered with various approaches, including optical, SAR and LiDAR sensors, giving a broader view of the environment. Even though CNNs work well, they also have some limitations. Labels on a large dataset are still quite hard to get, making it difficult for supervised learning. Making these datasets needs lots of resources, mainly in places that are seldom observed. In order to resolve this, researchers rely on transfer learning by using ImageNet trained models and adjusting them for their particular tasks. Some are investigating self-supervised and weakly supervised learning to require less labeling data. A further issue is that deep CNN models require a lot of computing resources for both training and deployment. It prevents some smaller institutions from using AI solutions. Therefore, new approaches and techniques to make models as lightweight as possible are being developed. In addition, explainability is still a main problem since CNNs are often difficult to explain how they work. Using tools such as Grad-CAM and saliency maps can explain why a model made certain decisions which builds confidence.  Right now, CNNs have many uses in environmental monitoring, though ongoing efforts are needed to deal with existing issues.},
      keywords = {Convolutional Neural Networks, Remote Sensing, Satellite Imagery, Image Classification, Deep Learning, Object Detection, Semantic Segmentation, Transfer Learning, Data Fusion, Environmental Monitoring},
      month = {December},
  }